Predicting mediaplan traffic
First Claim
1. A method comprising:
- receiving, by a traffic data store, first data indicative of first traffic data for a mediaplan during a first time span and second data indicative of second traffic data for the mediaplan during a second time span, the first and second traffic data representing numbers of unique entities that have interacted with a location during, respectively, the first and second time spans;
using, by a prediction engine, a function derived from at least one of data received from a user device, the received first data, and the received second data and one of an arbitrary number of unique entities or an arbitrary third time span to predict third traffic data representing a number of unique entities interacting with the location during the third time span; and
outputting the prediction of third traffic data to the mediaplan, the mediaplan associated with at least one of an advertising campaign and a media strategy;
where the function, when the arbitrary third time span does not exceed the first and second time spans, uses an exponential that includes a difference of dot products using natural logs of combinations of the first, second and arbitrary third time spans and the first and second traffic data, divided by a difference of natural logs of the first and second time spans; and
where the function otherwise uses a difference of dot products using combinations of the first, second and arbitrary third time spans and the first and second traffic data, divided by a difference of the first and second time spans.
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Accused Products
Abstract
Methods, systems, and apparatus for predicting traffic data, including a method comprising: receiving data indicative of traffic data for a mediaplan during first and second time spans, the data representing numbers of unique entities that have interacted with a location during the first and second time spans; and using a function to predict third traffic data during the third time span. The function, when the arbitrary third time span does not exceed the first and second time spans, uses an exponential that includes a difference of dot products using natural logs of combinations of the time spans and first and second traffic data, divided by a difference of natural logs of the first and second time spans. Otherwise, the function uses a difference of dot products using combinations of the time spans and the first and second traffic data, divided by a difference of the first and second time spans.
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Citations
30 Claims
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1. A method comprising:
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receiving, by a traffic data store, first data indicative of first traffic data for a mediaplan during a first time span and second data indicative of second traffic data for the mediaplan during a second time span, the first and second traffic data representing numbers of unique entities that have interacted with a location during, respectively, the first and second time spans; using, by a prediction engine, a function derived from at least one of data received from a user device, the received first data, and the received second data and one of an arbitrary number of unique entities or an arbitrary third time span to predict third traffic data representing a number of unique entities interacting with the location during the third time span; and outputting the prediction of third traffic data to the mediaplan, the mediaplan associated with at least one of an advertising campaign and a media strategy; where the function, when the arbitrary third time span does not exceed the first and second time spans, uses an exponential that includes a difference of dot products using natural logs of combinations of the first, second and arbitrary third time spans and the first and second traffic data, divided by a difference of natural logs of the first and second time spans; and where the function otherwise uses a difference of dot products using combinations of the first, second and arbitrary third time spans and the first and second traffic data, divided by a difference of the first and second time spans. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. A method comprising:
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determining first traffic data for a mediaplan during a first time span and second traffic data for the mediaplan during a second different time span, where the first and second different time spans are of substantially different lengths; storing at least one of the first traffic data and the second traffic data in a traffic data store; identifying an arbitrary time span over which predicted traffic data is desired to be determined; receiving, by a prediction engine, at least one of data received from a user device, the stored first traffic data, and the second traffic data; performing a curve fitting on the first traffic data and the second traffic data; determining, by a prediction engine, the predicted traffic over the arbitrary time span based on the curve fitting; and outputting the predicted traffic to the mediaplan, the mediaplan associated with at least one of an advertising campaign and a media strategy. - View Dependent Claims (19, 20, 21, 22)
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23. A method comprising:
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receiving traffic data at a traffic data store, identifying, using the stored traffic data, first traffic data Ut1 for a mediaplan S during a first time span t1 and second traffic data Ut2 for the mediaplan S during a second different time span t2, where the first time span t1 and the second different time span t2 are of substantially different lengths; identifying an arbitrary time span t over which predicted traffic data is desired to be determined; identifying a number of unique entities Ut that visited a particular media-plan in a duration of t days; predicting a number of unique entities Ut(S) that satisfy the mediaplan S over the arbitrary time span t including using the first traffic data Ut1 and the second traffic data Ut2 in accordance with the following formulas; for the arbitrary time span t less than or equal to t2 days; - View Dependent Claims (24, 25, 26, 27, 28)
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29. A system comprising:
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one or more processors communicatively coupled to a memory; one or more non-transitory computer-readable storage media configured to store instructions executable by said one or more processors; a prediction engine that uses prediction models stored in the storage media and transmitted to the memory for execution by said one or more processors and user traffic data to make predictions comprising predicting a number of unique entities Ut(S) that satisfy a mediaplan S over an arbitrary time span t including using first traffic data Ut1 during a first time span t1 and second traffic data Ut2 during a second different time span t2, where the first time span t1 and the second different time span t2 are of substantially different lengths, in accordance with the following formulas; for the arbitrary time span t less than or equal to t2 days;
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30. A computer program product tangibly embodied in a non-transitory computer-readable storage device and comprising instructions that, when executed by one or more processors, perform a method for predicting traffic for a mediaplan, the method comprising:
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identifying first traffic data Ut1 for a mediaplan S during a first time span t1 and second traffic data Ut2 for the mediaplan S during a second different time span t2, where the first time span t1 and the second different time span t2 are of substantially different lengths; identifying an arbitrary time span t over which predicted traffic data is desired to be determined; identifying a number of unique entities Ut that visited a particular media-plan in a duration of t days; and predicting, by a prediction engine, a number of unique entities Ut(S) that satisfy the mediaplan S over the arbitrary time span t including using the first traffic data Ut1 and the second traffic data Ut2 in accordance with the following formulas; for the arbitrary time span t less than or equal to t2 days;
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Specification